Object Picking Robot Training With Hybrid Real-Simulation Grasp Data
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Solution Overview
Problem
Existing machine learning-based robot training systems face inefficiencies due to limited data sources, relying solely on real-world or synthetic data, which reduces the accuracy and speed of robot grasping performance, and lacks integration of real and simulated data for training and runtime environments.
Innovation Solution
A hybrid machine learning approach that combines real and simulated grasp performance data to train robots, using a sensor feedback system for evaluating grasp quality and enabling online learning and self-correction, allowing for the assignment and refinement of grasp locations based on physical object properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only real-world robot picking experiments are utilized for training, then the training data is accurate and reliable, but the training time and computational resources are excessively consumed
Solution Approach 1:
The patent creates virtual copies of the real-world environment, robot, and objects through high-fidelity simulation. The simulation engine replicates physics, sensor models, and robot mechanics to generate synthetic training data that mirrors real-world conditions without requiring actual physical experiments, thus reducing training time while maintaining data reliability
Solution Approach 2:
The system performs preliminary training in the virtual environment before deploying to real-world applications. By pre-training the machine learning model using simulated data that incorporates real-world physical properties and sensor characteristics, the system prepares the robot in advance, reducing the need for extensive real-world trial and error
2Productivity
If only synthetic data generated in virtual environments is utilized for training, then the training speed is fast, but the accuracy and realism of grasp performance evaluation are reduced
Solution Approach 1:
The patent applies different data qualities to different training phases and scenarios. High-fidelity simulated data with realistic physics and sensor models is used where accuracy is critical, while faster-generated synthetic data is used for initial model development. The system selectively combines data sources based on the specific training needs and performance requirements
Solution Approach 2:
The training dataset is constructed as a composite of multiple data sources including high-fidelity simulation data, synthetic generated data, and real-world experiment data. This composite approach combines the advantages of each data source - the speed of synthetic generation, the realism of simulation, and the ground-truth accuracy of real experiments - to create a robust training corpus
3Productivity
If numerous sources of robot grasping performance data from actual and simulated experiments are cooperatively utilized, then the training efficiency and accuracy are improved, but the system complexity and data integration requirements increase
Solution Approach 1:
The patent develops a unified data framework and simulation engine that can handle multiple data sources through a single interface. The virtual environment is designed to accept various input formats from different sensors and experiment setups, processing them through common physics models and evaluation metrics, thus managing complexity while maintaining versatility in data integration
Solution Approach 2:
The simulation engine acts as an intermediary layer between diverse data sources and the machine learning training pipeline. It standardizes different data formats, applies consistent physics-based evaluation, and transforms heterogeneous inputs into a unified representation that the training system can process efficiently, reducing the burden of direct data integration
Data Source
AI summary
For training an object picking robot with real and simulated grasp performance data, grasp locations on an object are assigned based on object physical properties. A simulation experiment for robot grasping is performed using a first set of assigned locations. Based on simulation data from the simulation, a simulated object grasp quality of the robot is evaluated for each of the assigned locations. A first set of candidate grasp locations on the object is determined based on data representative of simulated grasp quality from the evaluation. Based on sensor data from an actual experiment for the robot grasping using each of the candidate grasp locations, an actual object grasp quality is evaluated for each of the candidate locations.